DBMS > Apache IoTDB vs. BigObject vs. Dgraph vs. RocksDB

System Properties Comparison Apache IoTDB vs. BigObject vs. Dgraph vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Analytic DBMS for real-time computations and queries

Distributed and scalable native Graph DBMS

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Graph DBMS

Key-value store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.bigobject.io

dgraph.io/docs

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

BigObject, Inc.

Dgraph Labs, Inc.

Facebook, Inc.

Initial release

2018

2015

2016

2013

Current release

1.1.0, April 2023

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

Open Source

Apache 2.0

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X
Windows

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC
Native API

fluentd
ODBC
RESTful HTTP API

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

C++ API
Java API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

none

Synchronous replication via Raft

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

yes

Planned for future releases

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